Ontology
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A Circular Construction Product Ontology for End-of-Life Decision-Making
2025-03-17 20:28:08Kwabena Adu-Duodu, Stanly Wilson, Yinhao Li, Aanuoluwapo Oladimeji, Talea Huraysi, Masoud Barati, Charith Perera, Ellis Solaiman, Omer Rana, Rajiv Ranjan, Tejal ShahAbstract
Efficient management of end-of-life (EoL) products is critical for advancing circularity in supply chains, particularly within the construction industry where EoL strategies are hindered by heterogenous lifecycle data and data silos. Current tools like Environmental Product Declarations (EPDs) and Digital Product Passports (DPPs) are limited by their dependency on seamless data integration and interoperability which remain significant challenges. To address these, we present the Circular Construction Product Ontology (CCPO), an applied framework designed to overcome semantic and data heterogeneity challenges in EoL decision-making for construction products. CCPO standardises vocabulary and facilitates data integration across supply chain stakeholders enabling lifecycle assessments (LCA) and robust decision-making. By aggregating disparate data into a unified product provenance, CCPO enables automated EoL recommendations through customisable SWRL rules aligned with European standards and stakeholder-specific circularity SLAs, demonstrating its scalability and integration capabilities. The adopted circular product scenario depicts CCPO's application while competency question evaluations show its superior performance in generating accurate EoL suggestions highlighting its potential to greatly improve decision-making in circular supply chains and its applicability in real-world construction environments.
Abstract (translated)
有效管理产品生命周期结束（EoL）阶段的产品对于推进供应链中的循环经济至关重要，尤其是在建筑业中，由于异质性生命周期数据和信息孤岛的存在，使得EoL策略的实施受到阻碍。当前工具如环境产品声明（EPDs）和数字产品护照（DPPs），受限于其依赖无缝数据集成与互操作性的需求，而这些仍然是重大挑战。为了应对这些问题，我们提出了循环建设产品本体论（CCPO），这是一个设计用来克服EoL决策中语义和数据异质性难题的应用框架。通过标准化词汇并促进供应链各方之间的数据整合，CCPO使得生命周期评估（LCA）及稳健的决策制定成为可能。 CCPO将分散的数据聚合为统一的产品来源，通过定制化的SWRL规则实现自动化的EoL建议，并且这些规则符合欧洲标准以及各利益相关方的具体循环经济服务级别协议。这展示了其可扩展性和集成能力。采用循环产品场景来描述CCPO的应用情况，而竞争力问题评估则表明了CCPO在生成准确的EoL建议方面的卓越性能，突显出它对于改进循环供应链中的决策制定及实际建筑环境应用具有巨大潜力。 总结来说，通过提供标准化的数据框架和自动化工具，CCPO能够帮助建筑业实现更加高效且环保的产品生命周期管理，从而支持循环经济的发展。
URL
https://arxiv.org/abs/2503.13708
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A representational framework for learning and encoding structurally enriched trajectories in complex agent environments
2025-03-17 14:04:27Corina Catarau-Cotutiu, Esther Mondragon, Eduardo AlonsoAbstract
The ability of artificial intelligence agents to make optimal decisions and generalise them to different domains and tasks is compromised in complex scenarios. One way to address this issue has focused on learning efficient representations of the world and on how the actions of agents affect them, such as disentangled representations that exploit symmetries. Whereas such representations are procedurally efficient, they are based on the compression of low-level state-action transitions, which lack structural richness. To address this problem, we propose to enrich the agent's ontology and extend the traditional conceptualisation of trajectories to provide a more nuanced view of task execution. Structurally Enriched Trajectories (SETs) extend the encoding of sequences of states and their transitions by incorporating hierarchical relations between objects, interactions and affordances. SETs are built as multi-level graphs, providing a detailed representation of the agent dynamics and a transferable functional abstraction of the task. SETs are integrated into an architecture, Structurally Enriched Trajectory Learning and Encoding (SETLE), that employs a heterogeneous graph-based memory structure of multi-level relational dependencies essential for generalisation. Using reinforcement learning as a data generation tool, we demonstrate that SETLE can support downstream tasks, enabling agents to recognise task-relevant structural patterns across diverse environments.
Abstract (translated)
在复杂场景中，人工智能代理做出最优决策并将这些决策推广到不同领域和任务的能力会受到限制。为解决这一问题，一种方法集中于学习世界的有效表示以及代理行动如何影响这个世界，例如通过利用对称性来实现解缠表示。尽管这样的表示方法从过程效率上讲是有效的，但它们基于低层次状态-动作转换的压缩，这些转换缺乏结构上的丰富性。为了应对这个问题，我们建议扩展代理的概念框架，并将传统的轨迹概念化延伸为提供更为细致的任务执行视角。结构性增强轨迹（SETs）通过引入对象之间、互动之间以及效用之间的层级关系来扩展序列状态及其转换的编码方式。SETs作为多层次图构建而成，提供了对代理动态变化的详细表示和可转移的功能抽象任务描述。 我们设计了一种名为结构化增强轨迹学习与编码（SETLE）的架构，该架构利用了多级关系依赖性的异构图形记忆结构，这是实现泛化所必需的关键因素。通过使用强化学习作为数据生成工具，我们证明了SETLE能够支持下游任务，并使代理能够在多种环境中识别出与任务相关的结构性模式。
URL
https://arxiv.org/abs/2503.13194
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Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes
2025-03-15 22:57:31Da Wu, Zhanliang Wang, Quan Nguyen, Kai WangAbstract
Background: Several studies show that large language models (LLMs) struggle with phenotype-driven gene prioritization for rare diseases. These studies typically use Human Phenotype Ontology (HPO) terms to prompt foundation models like GPT and LLaMA to predict candidate genes. However, in real-world settings, foundation models are not optimized for domain-specific tasks like clinical diagnosis, yet inputs are unstructured clinical notes rather than standardized terms. How LLMs can be instructed to predict candidate genes or disease diagnosis from unstructured clinical notes remains a major challenge. Methods: We introduce RAG-driven CoT and CoT-driven RAG, two methods that combine Chain-of-Thought (CoT) and Retrieval Augmented Generation (RAG) to analyze clinical notes. A five-question CoT protocol mimics expert reasoning, while RAG retrieves data from sources like HPO and OMIM (Online Mendelian Inheritance in Man). We evaluated these approaches on rare disease datasets, including 5,980 Phenopacket-derived notes, 255 literature-based narratives, and 220 in-house clinical notes from Childrens Hospital of Philadelphia. Results: We found that recent foundations models, including Llama 3.3-70B-Instruct and DeepSeek-R1-Distill-Llama-70B, outperformed earlier versions such as Llama 2 and GPT-3.5. We also showed that RAG-driven CoT and CoT-driven RAG both outperform foundation models in candidate gene prioritization from clinical notes; in particular, both methods with DeepSeek backbone resulted in a top-10 gene accuracy of over 40% on Phenopacket-derived clinical notes. RAG-driven CoT works better for high-quality notes, where early retrieval can anchor the subsequent reasoning steps in domain-specific evidence, while CoT-driven RAG has advantage when processing lengthy and noisy notes.
Abstract (translated)
背景：多项研究表明，大型语言模型（LLMs）在罕见疾病的表型驱动基因优先级排序方面存在困难。这些研究通常使用人类表型本体论（HPO）术语来提示像GPT和LLaMA这样的基础模型以预测候选基因。然而，在实际环境中，基础模型并未针对特定领域的任务（如临床诊断）进行优化，并且输入通常是未结构化的临床记录而非标准化的术语。如何指示LLMs从非结构化临床笔记中预测候选基因或疾病诊断仍然是一个重大挑战。 方法：我们介绍了两种结合了链式思维（CoT）和检索增强生成（RAG）的方法——由RAG驱动的CoT和由CoT驱动的RAG，以分析临床记录。一种五步问答式的CoT协议模拟专家推理过程，而RAG则从诸如HPO和OMIM这样的来源中提取数据。我们在罕见病的数据集上评估了这些方法的效果，包括5,980个来自Phenopacket的笔记、255个基于文献的故事以及来自费城儿童医院的220个内部临床记录。 结果：我们发现最近的基础模型（如Llama 3.3-70B-Instruct和DeepSeek-R1-Distill-Llama-70B）在候选基因优先级排序方面优于早期版本，例如Llama 2和GPT-3.5。我们也展示了由RAG驱动的CoT和由CoT驱动的RAG在这类任务上的表现均超过基础模型；特别是在基于DeepSeek架构的情况下，这两种方法对Phenopacket来源的临床记录在候选基因优先级排序中的前10名准确率超过了40%。由RAG驱动的CoT更适合高质量笔记，在这种情况下早期检索可以为后续推理步骤提供特定领域的证据依据，而由CoT驱动的RAG则更擅长处理长篇且复杂混乱的笔记。
URL
https://arxiv.org/abs/2503.12286
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Physics-based simulation ontology: an ontology to support modelling and reuse of data for physics-based simulation
Abstract
The current work presents an ontology developed for physics-based simulation in engineering design, called Physics-based Simulation Ontology (PSO). The purpose of the ontology is to assist in modelling the physical phenomenon of interest in a veridical manner, while capturing the necessary and reusable information for physics-based simulation solvers. The development involved extending an existing upper ontology, Basic Formal Ontology (BFO), to define lower-level terms of PSO. PSO has two parts: PSO-Physics, which consists of terms and relations used to model physical phenomena based on the perspective of classical mechanics involving partial differential equations, and PSO-Sim, which consists of terms used to represent the information artefacts that are about the physical phenomena modelled with PSO-Physics. The former terms are used to model the physical phenomenon of interest independent of solver-specific interpretations, which can be reused across different solvers, while the latter terms are used to instantiate solver-specific input data. A case study involving two simulation solvers was conducted to demonstrate this capability of PSO. Discussion around the benefits and limitations of using BFO for the current work is also provided, which should be valuable for any future work that extends an existing upper ontology to develop ontologies for engineering applications.
Abstract (translated)
目前的工作介绍了一种为工程设计中的基于物理的模拟开发的概念模型，称为基于物理的模拟本体（PSO）。该概念模型旨在以真实的方式帮助构建感兴趣的物理现象，并捕获用于基于物理的模拟求解器所需且可重复使用的信息。开发过程包括扩展一个现有的上层本体——基本形式本体（BFO），以便定义PSO中的低级术语。 PSO由两个部分组成：一是PSO-Physics，包含根据经典力学视角使用的术语和关系来建模物理现象的偏微分方程；二是PSO-Sim，包含了用于表示与使用PSO-Physics进行模型化的物理现象有关的信息制品的术语。前者被用来在独立于求解器特定解释的情况下对感兴趣的物理现象进行建模，并且可以在不同的求解器之间重复使用，而后者则用于实例化针对具体求解器的输入数据。 为了展示PSO的能力，进行了一个案例研究，涉及两个模拟求解器。此外还讨论了当前工作在使用BFO时的优势和局限性，这对未来的工作扩展现有上层本体以开发工程应用的概念模型应该具有参考价值。
URL
https://arxiv.org/abs/2503.11723
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Semantic Synergy: Unlocking Policy Insights and Learning Pathways Through Advanced Skill Mapping
2025-03-13 06:41:26Phoebe Koundouri, Conrad Landis, Georgios FeretzakisAbstract
This research introduces a comprehensive system based on state-of-the-art natural language processing, semantic embedding, and efficient search techniques for retrieving similarities and thus generating actionable insights from raw textual information. The system automatically extracts and aggregates normalized competencies from multiple documents (such as policy files and curricula vitae) and creates strong relationships between recognized competencies, occupation profiles, and related learning courses. To validate its performance, we conducted a multi-tier evaluation that included both explicit and implicit skill references in synthetic and real-world documents. The results showed near-human-level accuracy, with F1 scores exceeding 0.95 for explicit skill detection and above 0.93 for implicit mentions. The system thereby establishes a sound foundation for supporting in-depth collaboration across the AE4RIA network. The methodology involves a multi-stage pipeline based on extensive preprocessing and data cleaning, semantic embedding and segmentation via SentenceTransformer, and skill extraction using a FAISS-based search method. The extracted skills are associated with occupation frameworks (as formulated in the ESCO ontology) and with learning paths offered through the Sustainable Development Goals Academy. Moreover, interactive visualization software, implemented with Dash and Plotly, presents graphs and tables for real-time exploration and informed decision-making by those involved in policymaking, training and learning supply, career transitions, and recruitment. Overall, this system, backed by rigorous validation, offers promising prospects for improved policymaking, human resource development, and lifelong learning by providing structured and actionable insights from raw, complex textual information.
Abstract (translated)
这项研究介绍了一个基于最先进的自然语言处理、语义嵌入和高效搜索技术的综合系统，用于从原始文本信息中检索相似性并生成可操作见解。该系统能够自动从多个文档（如政策文件和个人简历）中提取并汇总标准化的能力，并创建已识别能力与职业档案及相关的学习课程之间的强关联关系。为了验证其性能，我们进行了多层次评估，其中包括合成和现实世界文档中的显性和隐性技能引用。结果显示，对于显式技能检测的F1分数超过0.95，而对于隐性提及则超过了0.93，这表明系统接近人类级别的准确性。 该系统为支持AE4RIA网络内的深入合作奠定了坚实的基础。其方法包括基于广泛的预处理和数据清洗、通过SentenceTransformer进行语义嵌入和分段以及使用FAISS搜索技术进行技能提取的多阶段流水线。所提取的技能与职业框架（根据ESCO本体论制定）相关联，并且与通过可持续发展目标学院提供的学习路径相关联。 此外，采用Dash和Plotly实现的交互式可视化软件提供了实时探索及政策制定、培训供应、职业生涯过渡以及招聘相关人员进行知情决策所需的图形和表格。总体而言，这一经过严格验证的系统为改进政策制定、人力资源发展以及终身学习提供有希望的前景，通过从复杂的原始文本信息中提取结构化且可操作的见解来实现这些目标。
URL
https://arxiv.org/abs/2503.10094
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Lawful and Accountable Personal Data Processing with GDPR-based Access and Usage Control in Distributed Systems
2025-03-10 10:49:34L. Thomas van Binsbergen, Marten C. Steketee, Milen G. Kebede, Heleen L. Janssen, Tom M. van EngersAbstract
Compliance with the GDPR privacy regulation places a significant burden on organisations regarding the handling of personal data. The perceived efforts and risks of complying with the GDPR further increase when data processing activities span across organisational boundaries, as is the case in both small-scale data sharing settings and in large-scale international data spaces. This paper addresses these concerns by proposing a case-generic method for automated normative reasoning that establishes legal arguments for the lawfulness of data processing activities. The arguments are established on the basis of case-specific legal qualifications made by privacy experts, bringing the human in the loop. The obtained expert system promotes transparency and accountability, remains adaptable to extended or altered interpretations of the GDPR, and integrates into novel or existing distributed data processing systems. This result is achieved by defining a formal ontology and semantics for automated normative reasoning based on an analysis of the purpose-limitation principle of the GDPR. The ontology and semantics are implemented in eFLINT, a domain-specific language for specifying and reasoning with norms. The XACML architecture standard, applicable to both access and usage control, is extended, demonstrating how GDPR-based normative reasoning can integrate into (existing, distributed) systems for data processing. The resulting system is designed and critically assessed in reference to requirements extracted from the GPDR.
Abstract (translated)
GDPR隐私法规的合规性对组织在处理个人数据方面带来了重大负担。当数据处理活动跨越组织边界时，比如小型数据共享场景和大型国际数据空间中，遵守GDPR所需的努力和风险会进一步增加。本文通过提出一种基于案例通用的方法来解决这些问题，该方法实现了自动化规范推理，并为数据处理活动的合法性建立法律依据。这些论点是根据隐私专家对特定案件的具体法律资格进行评估后得出的，将人类判断纳入其中。所获得的专家系统促进了透明度和问责制，能够适应扩展或改变的GDPR解释，并集成到新的或现有的分布式数据处理系统中。 这一结果通过定义一个基于对GDPR目的限制原则分析的自动化规范推理的形式本体论和语义学来实现。该形式化本体论和语义学在eFLINT（一种用于指定和进行规范推理的专业领域语言）中实施。XACML架构标准适用于访问控制和使用控制，该标准被扩展以展示基于GDPR的规范推理如何整合到数据处理系统（无论是现有还是分布式）。最终实现的系统根据从GDPR提取的需求进行了设计并接受批判性评估。
URL
https://arxiv.org/abs/2503.07172
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Ontology Generation using Large Language Models
2025-03-07 13:03:28Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskis\"arkk\"a, Sara Zuppiroli, Miguel Ceriani, Aldo Gangemi, Eva Blomqvist, Andrea Giovanni NuzzoleseAbstract
The ontology engineering process is complex, time-consuming, and error-prone, even for experienced ontology engineers. In this work, we investigate the potential of Large Language Models (LLMs) to provide effective OWL ontology drafts directly from ontological requirements described using user stories and competency questions. Our main contribution is the presentation and evaluation of two new prompting techniques for automated ontology development: Memoryless CQbyCQ and Ontogenia. We also emphasize the importance of three structural criteria for ontology assessment, alongside expert qualitative evaluation, highlighting the need for a multi-dimensional evaluation in order to capture the quality and usability of the generated ontologies. Our experiments, conducted on a benchmark dataset of ten ontologies with 100 distinct CQs and 29 different user stories, compare the performance of three LLMs using the two prompting techniques. The results demonstrate improvements over the current state-of-the-art in LLM-supported ontology engineering. More specifically, the model OpenAI o1-preview with Ontogenia produces ontologies of sufficient quality to meet the requirements of ontology engineers, significantly outperforming novice ontology engineers in modelling ability. However, we still note some common mistakes and variability of result quality, which is important to take into account when using LLMs for ontology authoring support. We discuss these limitations and propose directions for future research.
Abstract (translated)
本体工程过程复杂、耗时且容易出错，即使对于经验丰富的本体工程师也是如此。在这项工作中，我们探讨了大型语言模型（LLMs）在使用用户故事和胜任力问题描述的本体需求下直接生成有效的OWL本体现草稿的可能性。我们的主要贡献在于介绍并评估了两种新的自动化本体开发提示技术：无记忆CQbyCQ和Ontogenia。此外，我们还强调了三种用于本体评估的结构化标准的重要性，并突出了专家定性评价在捕捉生成本体的质量和可用性方面的需求，这表明多维度评估是必要的。 我们在一个基准数据集上进行了实验，该数据集中包含十个本体、100个不同的胜任力问题（CQs）和29种不同的用户故事。这些实验比较了三种LLMs在使用两种提示技术时的性能表现。结果表明，在大型语言模型支持下的本体工程领域中，它们超越了当前最先进的技术水平。具体而言，OpenAI o1-preview模型与Ontogenia方法生成的本体质量足够高，能够满足本体工程师的需求，并且建模能力显著优于新手本体工程师的表现。然而，我们仍然注意到一些常见的错误和结果质量的变化，在使用LLMs进行本体创作支持时需要予以考虑。本文讨论了这些限制并提出了未来研究的方向。
URL
https://arxiv.org/abs/2503.05388
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Building Knowledge Graphs Towards a Global Food Systems Datahub
2025-02-26 19:13:11Nirmal Gelal, Aastha Gautam, Sanaz Saki Norouzi, Nico Giordano, Claudio Dias da Silva Jr, Jean Ribert Francois, Kelsey Andersen Onofre, Katherine Nelson, Stacy Hutchinson, Xiaomao Lin, Stephen Welch, Romulo Lollato, Pascal Hitzler, Hande K\"u\c{c}\"uk McGintyAbstract
Sustainable agricultural production aligns with several sustainability goals established by the United Nations (UN). However, there is a lack of studies that comprehensively examine sustainable agricultural practices across various products and production methods. Such research could provide valuable insights into the diverse factors influencing the sustainability of specific crops and produce while also identifying practices and conditions that are universally applicable to all forms of agricultural production. While this research might help us better understand sustainability, the community would still need a consistent set of vocabularies. These consistent vocabularies, which represent the underlying datasets, can then be stored in a global food systems datahub. The standardized vocabularies might help encode important information for further statistical analyses and AI/ML approaches in the datasets, resulting in the research targeting sustainable agricultural production. A structured method of representing information in sustainability, especially for wheat production, is currently unavailable. In an attempt to address this gap, we are building a set of ontologies and Knowledge Graphs (KGs) that encode knowledge associated with sustainable wheat production using formal logic. The data for this set of knowledge graphs are collected from public data sources, experimental results collected at our experiments at Kansas State University, and a Sustainability Workshop that we organized earlier in the year, which helped us collect input from different stakeholders throughout the value chain of wheat. The modeling of the ontology (i.e., the schema) for the Knowledge Graph has been in progress with the help of our domain experts, following a modular structure using KNARM methodology. In this paper, we will present our preliminary results and schemas of our Knowledge Graph and ontologies.
Abstract (translated)
可持续农业生产的做法与联合国（UN）制定的多项可持续发展目标相吻合。然而，缺乏全面研究来考察不同产品和生产方法之间的可持续农业生产实践。此类研究能够提供关于特定作物和农产品影响因素的重要见解，并同时识别适用于所有形式农业生产的一般性做法和条件。尽管这项研究有助于我们更好地理解可持续性问题，社区仍然需要一套统一的术语集。这些代表基础数据集的统一术语可以存储在全球食品系统数据中心中。标准化的词汇可能有助于在数据集中编码用于进一步统计分析和AI/ML方法的重要信息，从而聚焦于可持续农业生产的科研目标。 目前尚无一种结构化的方式来表示有关可持续性的信息，特别是对于小麦生产而言。为了弥补这一空白，我们正在构建一套本体论（ontologies）和知识图谱（KGs），通过正式逻辑来编码与可持续小麦生产相关联的知识。这些知识图的数据来自公共数据来源、我们在堪萨斯州立大学进行的实验结果以及今年早些时候我们组织的一场可持续性研讨会，该研讨会有助于收集价值链中不同利益相关者的反馈。本体论（即知识图谱的模式）的设计工作已经通过采用KNARM方法并借助领域专家的帮助而展开。 在这篇论文中，我们将展示我们的初步成果和关于知识图及本体论的结构模型。
URL
https://arxiv.org/abs/2502.19507
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Synthia: Novel Concept Design with Affordance Composition
2025-02-25 02:54:11Xiaomeng Jin, Hyeonjeong Ha, Jeonghwan Kim, Jiateng Liu, Zhenhailong Wang, Khanh Duy Nguyen, Ansel Blume, Nanyun Peng, Kai-wei Chang, Heng JiAbstract
Text-to-image (T2I) models enable rapid concept design, making them widely used in AI-driven design. While recent studies focus on generating semantic and stylistic variations of given design concepts, functional coherence--the integration of multiple affordances into a single coherent concept--remains largely overlooked. In this paper, we introduce SYNTHIA, a framework for generating novel, functionally coherent designs based on desired affordances. Our approach leverages a hierarchical concept ontology that decomposes concepts into parts and affordances, serving as a crucial building block for functionally coherent design. We also develop a curriculum learning scheme based on our ontology that contrastively fine-tunes T2I models to progressively learn affordance composition while maintaining visual novelty. To elaborate, we (i) gradually increase affordance distance, guiding models from basic concept-affordance association to complex affordance compositions that integrate parts of distinct affordances into a single, coherent form, and (ii) enforce visual novelty by employing contrastive objectives to push learned representations away from existing concepts. Experimental results show that SYNTHIA outperforms state-of-the-art T2I models, demonstrating absolute gains of 25.1% and 14.7% for novelty and functional coherence in human evaluation, respectively.
Abstract (translated)
文本到图像（T2I）模型能够快速生成概念设计，因此在AI驱动的设计中被广泛应用。尽管最近的研究重点在于根据给定的设计概念生成语义和风格上的变化，但功能一致性——即将多个功能集成到单一连贯的概念中的能力——仍然很大程度上被忽视了。在这篇论文中，我们介绍了SYNTHIA框架，该框架旨在基于所需的功能需求来生成新颖且功能一致的设计。我们的方法利用了一个层次化的概念本体论，将设计概念分解为各个部分和功能，这是实现功能性连贯设计的关键构建块之一。此外，我们还开发了一种基于此本体论的课程学习方案，通过对比微调T2I模型来逐步学习功能组成，同时保持视觉新颖性。 具体来说，我们的方法包括： 1. 逐渐增加功能距离，引导模型从基本的概念-功能关联发展到复杂的多职能组合，将来自不同功能的部分整合为一个单一、连贯的整体形式。 2. 通过采用对比目标来强制执行视觉新颖性，使学习到的表示远离现有概念。 实验结果显示，SYNTHIA在新颖性和功能性一致性的人类评估中分别比最先进的T2I模型高出25.1%和14.7%，从而超越了现有的技术标准。
URL
https://arxiv.org/abs/2502.17793
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CVE-LLM : Ontology-Assisted Automatic Vulnerability Evaluation Using Large Language Models
2025-02-21 20:59:15Rikhiya Ghosh, Hans-Martin von Stockhausen, Martin Schmitt, George Marica Vasile, Sanjeev Kumar Karn, Oladimeji FarriAbstract
The National Vulnerability Database (NVD) publishes over a thousand new vulnerabilities monthly, with a projected 25 percent increase in 2024, highlighting the crucial need for rapid vulnerability identification to mitigate cybersecurity attacks and save costs and resources. In this work, we propose using large language models (LLMs) to learn vulnerability evaluation from historical assessments of medical device vulnerabilities in a single manufacturer's portfolio. We highlight the effectiveness and challenges of using LLMs for automatic vulnerability evaluation and introduce a method to enrich historical data with cybersecurity ontologies, enabling the system to understand new vulnerabilities without retraining the LLM. Our LLM system integrates with the in-house application - Cybersecurity Management System (CSMS) - to help Siemens Healthineers (SHS) product cybersecurity experts efficiently assess the vulnerabilities in our products. Also, we present guidelines for efficient integration of LLMs into the cybersecurity tool.
Abstract (translated)
国家漏洞数据库（NVD）每月发布超过一千种新漏洞，并预计在2024年增长25%，这突显了快速识别漏洞以减轻网络安全攻击、节省成本和资源的迫切需求。在这项工作中，我们提出使用大型语言模型（LLMs）从单一制造商产品组合中医疗设备漏洞的历史评估中学习漏洞评估。我们强调使用LLM进行自动漏洞评估的有效性和挑战，并介绍了一种方法，通过将历史数据与网络安全本体相结合来丰富这些数据，使系统能够在不重新训练LLM的情况下理解新出现的漏洞。我们的LLM系统集成到内部应用——网络安全管理系统（CSMS）中，以帮助西门子医疗保健公司（SHS）的产品网络安全专家高效评估我们产品的漏洞情况。此外，我们还提出了将LLMs有效整合到网络安全工具中的指南。
URL
https://arxiv.org/abs/2502.15932
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Concept Layers: Enhancing Interpretability and Intervenability via LLM Conceptualization
2025-02-19 11:10:19Or Raphael Bidusa, Shaul MarkovitchAbstract
The opaque nature of Large Language Models (LLMs) has led to significant research efforts aimed at enhancing their interpretability, primarily through post-hoc methods. More recent in-hoc approaches, such as Concept Bottleneck Models (CBMs), offer both interpretability and intervenability by incorporating explicit concept representations. However, these methods suffer from key limitations, including reliance on labeled concept datasets and significant architectural modifications that challenges re-integration into existing system pipelines. In this work, we introduce a new methodology for incorporating interpretability and intervenability into an existing model by integrating Concept Layers (CLs) into its architecture. Our approach projects the model's internal vector representations into a conceptual, explainable vector space before reconstructing and feeding them back into the model. Furthermore, we eliminate the need for a human-selected concept set by algorithmically searching an ontology for a set of concepts that can be either task-specific or task-agnostic. We evaluate CLs across multiple tasks, demonstrating that they maintain the original model's performance and agreement while enabling meaningful interventions. Additionally, we present a proof of concept showcasing an intervenability interface, allowing users to adjust model behavior dynamically, such as mitigating biases during inference.
Abstract (translated)
大型语言模型（LLM）的不透明性促使了大量旨在增强其可解释性的研究工作，主要通过事后方法实现。最近的在先方法，例如概念瓶颈模型（CBMs），通过引入明确的概念表示来提供可解释性和可控性。然而，这些方法面临着关键限制，包括依赖于标注的概念数据集以及需要显著架构修改，这挑战了重新整合到现有系统管道中的可行性。在这项工作中，我们介绍了一种新的方法，将可解释性和可控性融入现有的模型中，通过在模型架构中集成概念层（CLs）来实现这一目标。我们的方法将模型的内部向量表示投影到一个概念化、可解释的向量空间，并在其重构后反馈回模型中。此外，我们通过算法搜索本体论以找到一组可以是任务特定或与任务无关的概念集，从而消除了对人工选择概念集的需求。我们在多个任务上评估了CLs的有效性，证明它们在保持原始模型性能和一致性的同时，还能够实现有意义的干预操作。此外，我们还展示了一个概念验证，展示了可干预性界面的功能，允许用户动态调整模型行为，例如，在推理过程中减轻偏见。
URL
https://arxiv.org/abs/2502.13632
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Complex Ontology Matching with Large Language Model Embeddings
2025-02-19 10:56:27Guilherme Sousa, Rinaldo Lima, Cassia TrojahnAbstract
Ontology, and more broadly, Knowledge Graph Matching is a challenging task in which expressiveness has not been fully addressed. Despite the increasing use of embeddings and language models for this task, approaches for generating expressive correspondences still do not take full advantage of these models, in particular, large language models (LLMs). This paper proposes to integrate LLMs into an approach for generating expressive correspondences based on alignment need and ABox-based relation discovery. The generation of correspondences is performed by matching similar surroundings of instance sub-graphs. The integration of LLMs results in different architectural modifications, including label similarity, sub-graph matching, and entity matching. The performance word embeddings, sentence embeddings, and LLM-based embeddings, was compared. The results demonstrate that integrating LLMs surpasses all other models, enhancing the baseline version of the approach with a 45\% increase in F-measure.
Abstract (translated)
本论文探讨了本体论以及更广泛的知识图谱匹配中的一个挑战性任务，即表达能力尚未得到充分解决的问题。尽管嵌入和语言模型在这一任务中得到了越来越广泛的应用，但生成具有表现力的对应关系的方法仍未能充分利用这些模型，特别是大型语言模型（LLM）。本文提出了一种基于对齐需求和ABox（原子公理盒子）基关系发现来生成表达性对应关系的新方法，并将大型语言模型整合其中。该方法通过匹配实例子图的相似环境来进行对应关系的生成。 大型语言模型的集成带来了不同的架构修改，包括标签相似度、子图匹配以及实体匹配等方面的变化。研究比较了词嵌入、句子嵌入和基于LLM的嵌入在任务中的性能表现。实验结果表明，将大型语言模型整合进方法中可以超越其他所有模型，在F-measure（综合衡量精确率和召回率）上比基线版本的方法提高了45%。
URL
https://arxiv.org/abs/2502.13619
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Design an Ontology for Cognitive Business Strategy Based on Customer Satisfaction
2025-02-19 08:29:23Neda Bagherzadeh, Saeed Setayeshi, Samaneh YazdaniAbstract
Ontology is a general term used by researchers who want to share information in a specific domain. One of the hallmarks of the greatest success of a powerful manager of an organization is his ability to interpret unplanned and unrelated events. Tools to solve this problem are vital to business growth. Modern technology allows customers to be more informed and influential in their roles as patrons and critics. This can make or break a business. Research shows that businesses that employ a customer-first strategy and prioritize their customers can generate more revenue. Even though there are many different Ontologies offered to businesses, none of it is built from a cognitive perspective. The objective of this study is to address the concept of strategic business plans with a cognitive ontology approach as a basis for a new management tool. This research proposes to design a cognitive ontology model that links customer measurement with traditional business models, define relationships between components and verify the accuracy of the added financial value.
Abstract (translated)
本研究翻译如下： “本体论”是希望在特定领域共享信息的研究者常用的一个通用术语。一位组织中强大管理者最显著的成功标志之一就是他能够解读未计划和无关联的事件。解决此类问题的工具对于企业的增长至关重要。现代技术让客户能够在作为消费者和批评家的角色中更加知情且有影响力，这可能会决定一个企业的成败。研究表明，采用以客户为中心策略并优先考虑客户的业务可以带来更高的收入。尽管有许多不同版本的本体论供企业使用，但没有一种是从认知角度构建的。这项研究的目标是通过从认知本体论的角度来探讨战略商业计划的概念，并以此为基础创建一个新的管理工具。本研究旨在设计一个将客户测量与传统商业模式相连接的认知本体模型，定义组件之间的关系并验证所增加的财务价值的准确性。
URL
https://arxiv.org/abs/2503.05733
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Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering
2025-02-17 06:53:15Runxuan Liu, Bei Luo, Jiaqi Li, Baoxin Wang, Ming Liu, Dayong Wu, Shijin Wang, Bing QinAbstract
Large language models (LLMs) have shown remarkable capabilities in natural language processing. However, in knowledge graph question answering tasks (KGQA), there remains the issue of answering questions that require multi-hop reasoning. Existing methods rely on entity vector matching, but the purpose of the question is abstract and difficult to match with specific entities. As a result, it is difficult to establish reasoning paths to the purpose, which leads to information loss and redundancy. To address this issue, inspired by human reverse thinking, we propose Ontology-Guided Reverse Thinking (ORT), a novel framework that constructs reasoning paths from purposes back to conditions. ORT operates in three key phases: (1) using LLM to extract purpose labels and condition labels, (2) constructing label reasoning paths based on the KG ontology, and (3) using the label reasoning paths to guide knowledge retrieval. Experiments on the WebQSP and CWQ datasets show that ORT achieves state-of-the-art performance and significantly enhances the capability of LLMs for KGQA.
Abstract (translated)
大型语言模型（LLMs）在自然语言处理方面表现出色。然而，在知识图谱问答任务（KGQA）中，仍然存在多跳推理问题的挑战。现有的方法依赖于实体向量匹配，但问题是抽象且难以与特定实体进行匹配的。因此，建立从前提到目的的推理路径变得困难，这导致信息丢失和冗余。为了应对这一问题，受人类逆向思维启发，我们提出了一种新颖的框架——本体论指导逆向思考（ORT），该框架通过从目的反推回前提来构建推理路径。ORT主要分为三个关键阶段：(1) 利用LLM提取目的标签和条件标签；(2) 基于知识图谱的本体论构建标签推理路径；(3) 使用标签推理路径指导知识检索。在WebQSP和CWQ数据集上的实验表明，ORT达到了最先进的性能，并显著提升了LLMs在KGQA方面的能力。
URL
https://arxiv.org/abs/2502.11491
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The Vendiscope: An Algorithmic Microscope For Data Collections
Abstract
The evolution of microscopy, beginning with its invention in the late 16th century, has continuously enhanced our ability to explore and understand the microscopic world, enabling increasingly detailed observations of structures and phenomena. In parallel, the rise of data-driven science has underscored the need for sophisticated methods to explore and understand the composition of complex data collections. This paper introduces the Vendiscope, the first algorithmic microscope designed to extend traditional microscopy to computational analysis. The Vendiscope leverages the Vendi scores -- a family of differentiable diversity metrics rooted in ecology and quantum mechanics -- and assigns weights to data points based on their contribution to the overall diversity of the collection. These weights enable high-resolution data analysis at scale. We demonstrate this across biology, materials science, and machine learning (ML). We analyzed the $250$ million protein sequences in the protein universe, discovering that over $200$ million are near-duplicates and that AlphaFold fails on proteins with Gene Ontology (GO) functions that contribute most to diversity. Applying the Vendiscope to the Materials Project database led to similar findings: more than $85\%$ of the crystals with formation energy data are near-duplicates and ML models perform poorly on materials that enhance diversity. Additionally, the Vendiscope can be used to study phenomena such as memorization in generative models. We used the Vendiscope to identify memorized training samples from $13$ different generative models and found that the best-performing ones often memorize the training samples that contribute least to diversity. Our findings demonstrate that the Vendiscope can serve as a powerful tool for data-driven science.
Abstract (translated)
自16世纪末显微镜发明以来，显微技术的演进不断增强了我们探索和理解微观世界的能力，使结构和现象的观察越来越精细。与此同时，数据驱动科学的发展强调了对复杂数据集合进行探究与理解的先进方法的需求。本文介绍了Vendiscope，这是第一种将传统显微分析扩展到计算分析领域的算法显微镜。 Vendiscope利用了Vendi评分——一组源自生态学和量子力学的可微多样性度量，并根据其贡献于整体多样性的程度为数据点分配权重。这些权重使得大规模高分辨率数据分析成为可能。我们在生物学、材料科学以及机器学习（ML）领域展示了这一点。 我们分析了蛋白质宇宙中的2.5亿个蛋白序列，发现超过2亿种是近似重复的，并且AlphaFold在处理那些最能增加多样性的Gene Ontology (GO)功能蛋白时表现不佳。当应用于Materials Project数据库中具有形成能量数据的晶格体时，Vendiscope得出了类似的结论：超过85%的晶体是近似重复的，而机器学习模型对增强多样性的重要材料处理效果较差。 此外，Vendiscope还可用于研究生成模型中的记忆现象。我们使用Vendiscope从13种不同的生成模型中识别了被记住的训练样本，并发现表现最佳的那些模型往往记住的是贡献最少于多样性的训练样本。 这些发现表明，Vendiscope可以作为数据驱动科学研究的强大工具。
URL
https://arxiv.org/abs/2502.10828
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Data2Concept2Text: An Explainable Multilingual Framework for Data Analysis Narration
2025-02-13 11:49:48Flavio Bertini (UNIPR), Alessandro Dal Pal\`u (UNIPR), Federica Zaglio (UNIPR), Francesco Fabiano (NMSU), Andrea Formisano (UNIUD)Abstract
This paper presents a complete explainable system that interprets a set of data, abstracts the underlying features and describes them in a natural language of choice. The system relies on two crucial stages: (i) identifying emerging properties from data and transforming them into abstract concepts, and (ii) converting these concepts into natural language. Despite the impressive natural language generation capabilities demonstrated by Large Language Models, their statistical nature and the intricacy of their internal mechanism still force us to employ these techniques as black boxes, forgoing trustworthiness. Developing an explainable pipeline for data interpretation would allow facilitating its use in safety-critical environments like processing medical information and allowing non-experts and visually impaired people to access narrated information. To this end, we believe that the fields of knowledge representation and automated reasoning research could present a valid alternative. Expanding on prior research that tackled the first stage (i), we focus on the second stage, named Concept2Text. Being explainable, data translation is easily modeled through logic-based rules, once again emphasizing the role of declarative programming in achieving AI explainability. This paper explores a Prolog/CLP-based rewriting system to interpret concepts-articulated in terms of classes and relations, plus common knowledge-derived from a generic ontology, generating natural language text. Its main features include hierarchical tree rewritings, modular multilingual generation, support for equivalent variants across semantic, grammar, and lexical levels, and a transparent rule-based system. We outline the architecture and demonstrate its flexibility through some examples capable of generating numerous diverse and equivalent rewritings based on the input concept.
Abstract (translated)
本文介绍了一个完整的可解释系统，该系统能够解析一组数据，提炼出其底层特征，并以选定的自然语言描述这些特征。此系统的运行依赖于两个关键阶段：(i) 从数据中识别出现的属性并将其转换为抽象概念；(ii) 将这些概念转化为自然语言表述。 尽管大型语言模型展示了令人印象深刻的自动生成自然语言的能力，但它们统计性的本质以及内部机制的复杂性仍然迫使我们不得不像“黑盒子”一样使用它们，从而放弃了对其的信任。开发一个用于数据解释的可解释流水线将有助于在医疗信息处理等安全关键领域中应用该系统，并允许非专业人士及视障人士访问叙述化信息。 为此，我们认为知识表示和自动化推理研究领域的成果可能提供一种有效的替代方案。在此前的研究基础上探讨了第一阶段(i)的内容后，本文重点关注第二阶段，名为Concept2Text。由于其可解释性，数据翻译能够通过基于逻辑的规则轻松建模，再次强调声明式编程在实现AI可解释性中的重要角色。 本论文探索了一种基于Prolog/CLP（约束逻辑编程）的重写系统，该系统可以解读以类和关系术语及通用本体派生出的常见知识来表达的概念，并生成自然语言文本。其主要特性包括层次树重写、模块化多语种生成、支持在语义、语法和词汇层面上等效变体以及透明规则系统的使用。 本文概述了该架构并通过一些示例展示了它的灵活性，这些示例如何能够根据输入概念生成多种不同且等效的重写。
URL
https://arxiv.org/abs/2502.09218
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AI Enhanced Ontology Driven NLP for Intelligent Cloud Resource Query Processing Using Knowledge Graphs
2025-02-10 02:15:13Krishna Chaitanya Sunkara (Independent Researcher), Krishnaiah Narukulla (Independent Researcher)Abstract
The conventional resource search in cloud infrastructure relies on keyword-based searches or GUIDs, which demand exact matches and significant user effort to locate resources. These conventional search approaches often fail to interpret the intent behind natural language queries, making resource discovery inefficient and inaccessible to users. Though there exists some form of NLP based search engines, they are limited and focused more on analyzing the NLP query itself and extracting identifiers to find the resources. But they fail to search resources based on their behavior or operations or their capabilities or relationships or features or business relevance or the dynamic changing state or the knowledge these resources have. The search criteria has been changing with the inundation of AI based services which involved discovering not just the requested resources and identifiers but seeking insights. The real intent of a search has never been to just to list the resources but with some actual context such as to understand causes of some behavior in the system, compliance checks, capacity estimations, network constraints, or troubleshooting or business insights. This paper proposes an advanced Natural Language Processing (NLP) enhanced by ontology-based semantics to enable intuitive, human-readable queries which allows users to actually discover the intent-of-search itself. By constructing an ontology of cloud resources, their interactions, and behaviors, the proposed framework enables dynamic intent extraction and relevance ranking using Latent Semantic Indexing (LSI) and AI models. It introduces an automated pipeline which integrates ontology extraction by AI powered data crawlers, building a semantic knowledge base for context aware resource discovery.
Abstract (translated)
传统的云计算基础设施资源搜索依赖于基于关键词的搜索或全局唯一标识符（GUID），这要求精确匹配并且需要用户付出大量努力来定位资源。这些传统搜索方法通常无法理解自然语言查询背后的意图，导致资源发现效率低下且难以让用户使用。虽然存在一些基于NLP的搜索引擎，但它们主要集中在分析自然语言查询本身并提取标识符以查找资源，而不能根据资源的行为、操作、能力、关系或特性等进行搜索，也不能考虑到业务相关性或这些资源动态变化的状态和知识。 随着AI服务激增，搜索标准也发生了变化。如今需要的不仅仅是发现请求的资源和标识符，还需要获取洞察信息。真正的查询意图不仅在于列出资源，更要在理解系统中某些行为的原因、合规检查、容量估算、网络限制、故障排除或业务洞察等实际上下文方面。 本文提出了一种由本体语义增强的自然语言处理（NLP）方法，使用户能够创建直观且易于人类阅读的查询，从而真正发现查询意图。通过构建云计算资源及其交互和行为的本体，所提出的框架可以利用潜在语义索引（LSI）及AI模型进行动态意图提取和相关性排序。该框架引入了一个自动化管道，由人工智能驱动的数据爬虫自动提取本体，并建立一个语境感知型的知识库以实现上下文相关的资源发现。
URL
https://arxiv.org/abs/2502.18484
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Group Reasoning Emission Estimation Networks
2025-02-08 09:02:43Yanming Guo, Xiao Qian, Kevin Credit, Jin MaAbstract
Accurate greenhouse gas (GHG) emission reporting is critical for governments, businesses, and investors. However, adoption remains limited particularly among small and medium enterprises due to high implementation costs, fragmented emission factor databases, and a lack of robust sector classification methods. To address these challenges, we introduce Group Reasoning Emission Estimation Networks (GREEN), an AI-driven carbon accounting framework that standardizes enterprise-level emission estimation, constructs a large-scale benchmark dataset, and leverages a novel reasoning approach with large language models (LLMs). Specifically, we compile textual descriptions for 20,850 companies with validated North American Industry Classification System (NAICS) labels and align these with an economic model of carbon intensity factors. By reframing sector classification as an information retrieval task, we fine-tune Sentence-BERT models using a contrastive learning loss. To overcome the limitations of single-stage models in handling thousands of hierarchical categories, we propose a Group Reasoning method that ensembles LLM classifiers based on the natural NAICS ontology, decomposing the task into multiple sub-classification steps. We theoretically prove that this approach reduces classification uncertainty and computational complexity. Experiments on 1,114 NAICS categories yield state-of-the-art performance (83.68% Top-1, 91.47% Top-10 accuracy), and case studies on 20 companies report a mean absolute percentage error (MAPE) of 45.88%. The project is available at: this https URL.
Abstract (translated)
准确的温室气体（GHG）排放报告对于政府、企业和投资者来说至关重要。然而，由于高昂的实施成本、碎片化的排放因子数据库以及缺乏强大的行业分类方法等原因，其应用在中小型企业中仍然有限。为了解决这些挑战，我们引入了基于人工智能驱动的碳会计框架——集团推理排放估算网络（GREEN）。该框架标准化企业级别的排放估算，构建大规模基准数据集，并采用一种新颖的大规模语言模型（LLM）推理方法。 具体而言，我们收集了20,850家具有验证后的北美行业分类系统（NAICS）标签的公司文本描述，并将其与碳强度因素的经济模型对齐。通过将行业分类重新定义为信息检索任务，我们使用对比学习损失微调Sentence-BERT模型。为了克服单一阶段模型在处理数千个层级类别时的局限性，我们提出了一种基于自然NAICS本体论的集团推理方法，该方法集合了大规模语言模型分类器，并将任务分解成多个子分类步骤。理论上证明，这种方法可以降低分类不确定性并减少计算复杂度。 实验显示，在1,114个NAICS类别上的测试中，我们的方法达到了最先进的性能（Top-1准确率为83.68%，Top-10准确率为91.47%）。针对20家公司的案例研究显示，平均绝对百分比误差（MAPE）为45.88%。该项目的详细信息可在此URL访问：this https URL。
URL
https://arxiv.org/abs/2502.06874
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OntoTune: Ontology-Driven Self-training for Aligning Large Language Models
2025-02-08 07:38:45Zhiqiang Liu, Chengtao Gan, Junjie Wang, Yichi Zhang, Zhongpu Bo, Mengshu Sun, Huajun Chen, Wen ZhangAbstract
Existing domain-specific Large Language Models (LLMs) are typically developed by fine-tuning general-purposed LLMs with large-scale domain-specific corpora. However, training on large-scale corpora often fails to effectively organize domain knowledge of LLMs, leading to fragmented understanding. Inspired by how humans connect concepts and organize knowledge through mind maps, we aim to emulate this approach by using ontology with hierarchical conceptual knowledge to reorganize LLM's domain knowledge. From this perspective, we propose an ontology-driven self-training framework called OntoTune, which aims to align LLMs with ontology through in-context learning, enabling the generation of responses guided by the ontology. We leverage in-context learning to identify whether the LLM has acquired the specific concept's ontology knowledge, and select the entries not yet mastered by LLM as the training set to further align the LLM with ontology. Compared to existing domain LLMs based on newly collected large-scale domain-specific corpora, our OntoTune, which relies on the existing, long-term developed ontology and LLM itself, significantly reduces data maintenance costs and offers improved generalization ability. We conduct our study in the medical domain to evaluate the effectiveness of OntoTune, utilizing a standardized medical ontology, SNOMED CT as our ontology source. Experimental results demonstrate that OntoTune achieves state-of-the-art performance in both in-ontology task hypernym discovery and out-of-ontology task medical domain QA. Moreover, compared to the latest direct ontology injection method TaxoLLaMA, our OntoTune better preserves original knowledge of LLM. The code and data are available at this https URL.
Abstract (translated)
现有的领域特定大型语言模型（LLMs）通常是通过使用大规模的领域特定语料库对通用型LLM进行微调来开发的。然而，基于大规模语料库的训练往往无法有效组织LLM中的领域知识，导致其理解碎片化。受到人类通过思维导图连接概念和组织知识方式的启发，我们旨在通过采用具有层次化概念知识的本体论（ontology）重新组织LLM的领域知识来模仿这一方法。从这个角度来看，我们提出了一种名为OntoTune的基于本体论驱动的自我训练框架，其目标是通过上下文学习使LLM与本体论对齐，并能够生成由本体论指导的回答。我们利用上下文学习识别LLM是否已经掌握了特定概念的本体知识，并选择尚未掌握的概念作为训练集以进一步将LLM与本体论进行对齐。相比于基于新收集的大规模领域特异语料库构建现有领域的LLMs，我们的OntoTune依赖于长期开发的本体论和原有的LLM本身，在显著降低数据维护成本的同时提供了更好的泛化能力。 我们在医疗领域开展了这项研究，以评估OntoTune的有效性，并使用了标准化的医学本体论SNOMED CT作为我们的本体论来源。实验结果表明，OntoTune在本体内的同义词发现任务和本体外的任务——即医学领域的问答任务中都取得了当前最佳的表现。此外，与最新的直接本体注入方法TaxoLLaMA相比，我们的OntoTune更好地保留了原始LLM的知识。 代码和数据可在以下链接获取：[此URL]（请将“this https URL”替换为实际的URL）。
URL
https://arxiv.org/abs/2502.05478
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ApplE: An Applied Ethics Ontology with Event Context
2025-02-07 17:34:50Aisha Aijaz, Raghava Mutharaju, Manohar KumarAbstract
Applied ethics is ubiquitous in most domains, requiring much deliberation due to its philosophical nature. Varying views often lead to conflicting courses of action where ethical dilemmas become challenging to resolve. Although many factors contribute to such a decision, the major driving forces can be discretized and thus simplified to provide an indicative answer. Knowledge representation and reasoning offer a way to explicitly translate abstract ethical concepts into applicable principles within the context of an event. To achieve this, we propose ApplE, an Applied Ethics ontology that captures philosophical theory and event context to holistically describe the morality of an action. The development process adheres to a modified version of the Simplified Agile Methodology for Ontology Development (SAMOD) and utilizes standard design and publication practices. Using ApplE, we model a use case from the bioethics domain that demonstrates our ontology's social and scientific value. Apart from the ontological reasoning and quality checks, ApplE is also evaluated using the three-fold testing process of SAMOD. ApplE follows FAIR principles and aims to be a viable resource for applied ethicists and ontology engineers.
Abstract (translated)
应用伦理学在大多数领域中普遍存在，由于其哲学性质，需要进行大量的讨论和思考。不同的观点往往导致行动方案的冲突，在这种情况下解决道德困境变得具有挑战性。尽管许多因素会影响此类决策，但主要驱动因素可以被抽象化并简化为提供指示性的答案。知识表示与推理能够将抽象的伦理概念明确地转化为具体事件背景下的实用原则。为此，我们提出了ApplE（应用伦理学本体），它捕捉哲学理论和事件情境，以全面描述某一行动的道德性。开发过程遵循简化的敏捷方法论（Simplified Agile Methodology for Ontology Development, SAMOD）的一种修改版本，并采用了标准的设计和发布实践。 使用ApplE，我们从生物伦理领域建模了一个用例，展示了本体的社会价值和科学价值。除了进行本体推理和质量检查之外，还根据SAMOD的三重测试过程对ApplE进行了评估。ApplE遵循FAIR原则（即数据应具备可发现性、可访问性、互操作性和可重用性），旨在成为应用伦理学家和本体工程师的一个可行资源。
URL
https://arxiv.org/abs/2502.05110
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